Data management
Data management is central to HEXstream’s mission to transform streams of operational information into actionable, real-time business intelligence. The concept of data management is multi-faceted, from strategically ingesting, integrating and consolidating operational data, to visualizing that information with models that enable utilities to actually benefit from the insights generated.
Data Integration and Consolidation
Why Data Quality Management is Essential for Analytics
HEXstream just launched Koios™, a new Data Quality Management solution designed to build trust in enterprise data by automatically scrubbing data across silos to eliminate duplicates and inaccuracies.
Partnering with Kinetica for Advanced Analytics Excellence
Kinetica, one of HEXstream’s partners, is an advanced GPU-based database which enables advanced filtering, aggregation, and visualization capabilities. It is a columnar, memory-first database that is optimized for both CPU and GPU processing. It is useful for streaming analytics, artificial intelligence, machine learning, geospatial analytics, and many other functions. For the last six months, my team and I have worked with a large retail client to help them decrease the response times of their customer-facing applications. Building these applications using cutting-edge technologies like Spark, Kafka, and Java Spring Boot has increased customer satisfaction by making them much faster and more responsive.
Utility Analytics: Everything You Need To Know
How to Process Handwritten Text Using Python and Cloud Vision
With the size and scope of collected data expanding with each passing day, it is very important to continually analyze available data to drive better-informed business and policy decisions. To a large extent, handwritten data remains unexplored and unanalyzed. If we can analyze handwritten text data, we can minimize the hurdles and save the manpower involved in digitizing handwritten data.
How Utilities Can Leverage Feeder Circuit Data to Address Key Operational Questions
The Emerging Benefits Of Proper Integration In Evolving Utilities
Unleashing the Benefits of Real-Time Maximo-OFS Integration in Utilities
Research Report Launch: What Is The State Of Utilities In 2024?
Increased Complexity For All Utilities, And Unprecedented Opportunities For The Smart Ones
7 Ways Smarter Data Analytics Can Help City Managers
Data Visualization
An Introduction to Denodo for Data Virtualization
Info Fields: Improving Data Visualization for Utility Analytics and Beyond
In most, if not all, out-of-the-box databases, the data are typically designed for application architecture and optimized for performance. With that said, the data are rarely organized in the ideal format for reporting purposes. To better prepare the data for analytics, analysts use Dimension and Fact tables, in which many info fields materialize and are then decommissioned. While many do not recognize the term info field, info fields already exist in your organization’s dashboards and reports.
How to Choose the Right Machine Learning Algorithm
Selecting the correct machine learning algorithm can be difficult, but doing so is critical in order to answer a given question with high speed and accuracy. In this blog, we will introduce three types of machine learning algorithms and explain how to select the right one when tackling business problems. Then, we share a few real-world use cases for the different algorithm types.
An Intro To LEC & Potential Wins At Your Utility
Tips For Building Scalable Integrations In Oracle Integration Cloud (OIC)
What Are The Benefits Of Drill-through In Power BI?
First Splash: An Intro To Non-Revenue Water Opportunities
Advanced Oracle Utility Analytics Visualization (OUAV) Reporting: Designing Data-Driven Solutions For The Future Of Utilities
Tech Corner Tutorial: Using Databricks Delta Lake To Turn Raw Data Into Real Answers
The “Single Pane Of Glass” For True Visibility Among Utilities
Data Lake
Why Analytic Goals Should Drive Data Architecture Selection
When my kids were younger, the sheer volume of Legos scattered hazardously around the house demanded near-constant cleanup, which always included storing and organizing the bricks. For kids eager to speed through cleanup, organizing the bricks by color, rather than by type or by the kit they came in, would appear to be the optimal Lego storage strategy. But when playtime came back around, they learned the hard way that storing thousands of individual Legos by color made it nearly impossible to locate the bricks they needed in order to build functional structures.
Tech Corner Tutorial: Using Databricks Delta Lake To Turn Raw Data Into Real Answers
The Big Data Trio: Understanding Avro, Parquet And ORC In Simple Terms
The Best Data-Replication Tools Powering Real-Time Enterprise Intelligence
Data Warehouse
With years of experience working with time and mission-critical data for some of the largest utilities in the world, we have perfected our data-warehouse design for optimized performance with real-time data ingestion and data processing. We have transformed the traditional data-warehouse design by harnessing cutting-edge technologies, enabling faster insights, smarter decision-making, and a future-ready foundation for scalable data growth.